Democratizing landslide detection for vulnerable regions beyond resource-intensive foundation models

Landslides claim thousands of lives and cause billions in economic losses annually, with impacts disproportionately concentrated in developing regions across Asia, Africa, and Latin America. Paradoxically, the current trajectory of artificial intelligence in geohazard detection—characterized by billion-parameter foundation models requiring substantial computational infrastructure—risks widening, rather than closing, the gap between technological capability and operational deployment where it is needed most. We argue that this paradigm requires fundamental reconsideration, proposing domain adaptation on strategically curated geological datasets as a more equitable and effective path toward globally accessible landslide detection systems.

Foundation models like the Segment Anything Model (SAM), pre-trained on over one billion masks, demand computational resources—312 million parameters, 1,376 GFLOPs per inference, specialized GPU infrastructure—that remain inaccessible to disaster management agencies in resource-constrained regions. Beyond these practical constraints, we contend that the apparent generalization capabilities of such models reflect pattern coverage in training data rather than emergent understanding transferable to geological contexts. The SA-1B dataset, despite its scale, was not curated to systematically represent landslide morphological diversity, creating coverage gaps for rare failure types, unusual triggering mechanisms, and underrepresented terrain configurations precisely where robust detection is operationally critical.

Given these limitations, we propose that effective generalization for geological applications emerges not from architectural scale but from strategic coverage of domain-relevant pattern space. We developed and tested GeoNeXt, a lightweight architecture that exploits the hierarchical transferability of geological features through targeted domain adaptation. Low-level representations (edges, spectral gradients) transfer universally across sensors and terrain; mid-level patterns (drainage networks, slope morphology) require adaptation to local expressions; and high-level configurations (failure geometries, trigger signatures) demand targeted training. Our results showed that this approach outperformed SAM-based methods across three independent benchmarks while requiring 10× fewer parameters (32.2M versus 312.5M) and a 62% reduction in computational cost. Zero-shot transferability to geographically distinct test sites (74–78% F1 score) emerged from the training dataset's systematic morphological diversity rather than parameter count. Inference at 10.6 frames per second on standard hardware, versus 3.0 frames per second for foundation model alternatives, transforms theoretical capability into deployable technology for resource-constrained environments. These findings suggest that strategic domain adaptation, rather than architectural scale, offers the most viable path toward operational landslide detection in vulnerable regions.